Advertisement selection system supporting discretionary target market characteristics

ABSTRACT

An advertisement selection system is presented in which vectors describing an actual or hypothetical market for a product or desired viewing audience can be determined. An ad characterization vector is transmitted along with a consumer ID. The consumer ID is used to retrieve a consumer characterization vector which is correlated with the ad characterization vector to determine the suitability of the advertisement to the consumer. The consumer characterization vector describes statistical information regarding the demographics and product purchase preferences of a consumer, and is developed from previous purchases or viewing habits. A price for displaying the advertisement can be determined based on the results of the correlation of the ad characterization vector with the consumer characterization vector.

[0001] This application is a continuation of U.S. patent applicationSer. No. 09/268,526 filed on Mar. 12, 1999.

BACKGROUND OF THE INVENTION

[0002] The advent of the Internet has resulted in the ability tocommunicate data across the globe instantaneously, and will allow fornumerous new applications which enhance consumer's lives. One of theenhancements which can occur is the ability for the consumer to receiveadvertising which is relevant to their lifestyle, rather than a streamof ads determined by the program they are watching. Such “targeted ads”can potentially reduce the amount of unwanted information whichconsumers receive in the mail, during television programs, and whenusing the Internet.

[0003] From an advertiser's perspective the ability to target ads can bebeneficial since they have some confidence that their ad will at leastbe determined relevant by the consumer, and therefore will not be foundannoying because it is not applicable to their lifestyle.

[0004] In order to determine the applicability of an advertisement to aconsumer, it is necessary to know something about their lifestyle, andin particular to understand their demographics (age, household size,income). In some instances it is useful to know their particularpurchasing habits. As an example, a vendor of soups would like to knowwhich consumers are buying their competitor's soup, so that they cantarget ads at those consumers in an effort to convince them to switchbrands. That vendor will probably not want to target loyal customers,although for a new product introduction the strategy may be to convinceloyal customers to try the new product. In both cases it is extremelyuseful for the vendor to be able to determine what brand of product theconsumer presently purchases.

[0005] There are several difficulties associated with the collection,processing, and storage of consumer data. First, collecting consumerdata and determining the demographic parameters of the consumer can bedifficult. Surveys can be performed, and in some instances the consumerwill willingly give access to normally private data including familysize, age of family members, and household income. In such circumstancesthere generally needs to be an agreement with the consumer regarding howthe data will be used. If the consumer does not provide this datadirectly, the information must be “mined” from various pieces ofinformation which are gathered about the consumer, typically fromspecific purchases.

[0006] Once data is collected, usually from one source, some type ofprocessing can be performed to determine a particular aspect of theconsumer's life. As an example, processing can be performed on creditdata to determine which consumers are a good credit risk and haverecently applied for credit. The resulting list of consumers can besolicited, typically by direct mail.

[0007] Although information such as credit history is stored on multipledatabases, storage of other information such as the specifics of grocerypurchases is not typically performed. Even if each individual's detailedlist of grocery purchases was recorded, the information would be oflittle use since it would amount to nothing more than unprocessedshopping lists.

[0008] Privacy concerns are also an important factor in using consumerpurchase information. Consumers will generally find it desirable thatadvertisements and other information is matched with their interests,but will not allow indiscriminate access to their demographic profileand purchase records.

[0009] The Internet has spawned the concept of “negatively pricedinformation” in which consumers can be paid to receive advertising.Paying consumers to watch advertisements can be accomplishedinteractively over the Internet, with the consumer acknowledging thatthey will watch an advertisement for a particular price. Previouslyproposed schemes such as that described in U.S. Pat. No. 5,794,210,entitled “Attention Brokerage,” of which A. Nathaniel Goldhaber and GaryFitts are the inventors, describe such a system, in which the consumeris presented with a list of advertisements and their correspondingpayments. The consumer chooses from the list and is compensated forviewing the advertisement. The system requires real-time interactivityin that the viewer must select the advertisement from the list ofchoices presented.

[0010] The ability to place ads to consumers and compensate them forviewing the advertisements opens many possibilities for new models ofadvertising. However, it is important to understand the demographics andproduct preferences of the consumer in order to be able to determine ifan advertisement is appropriate.

[0011] Although it is possible to collect statistical informationregarding consumers of particular products and compare those profilesagainst individual demographic data points of consumers, such amethodology only allows for selection of potential consumers based onthe demographics of existing customers of the same or similar products.U.S. Pat. No. 5,515,098, entitled “System and method for selectivelydistributing commercial messages over a communications network,” ofwhich John B. Carles is the inventor, describes a method in which targethousehold data of actual customers of a product are compared againstsubscriber household data to determine the applicability of a commercialto a household. It will frequently be desirable to target anadvertisement to a market having discretionary characteristics and toobtain a measure of the correlation of these discretionary features withprobabilistic or deterministic data of the consumer/subscriber, ratherthan being forced to rely on the characteristics of existing consumersof a product. Such correlations should be possible based both ondemographic characteristics and product preferences.

[0012] Another previously proposed system, described in U.S. Pat. No.5,724,521, entitled “Method and apparatus for providing electronicadvertisements to end users in a consumer best-fit pricing manner,” ofwhich R. Dedrick is the inventor, utilizes a consumer scale as themechanism to determine to which group and advertisement is intended.Such a system requires specification of numerous parameters andweighting factors, and requires access to specific and non-statisticalpersonal profile information.

[0013] For the foregoing reasons, there is a need for an advertisementselection system which can match an advertisement with discretionarytarget market characteristics, and which can do so in a manner whichprotects the privacy of the consumer data and characterizations.

SUMMARY OF THE INVENTION

[0014] The present invention describes a system for determining theapplicability of an advertisement to a consumer, based on the receptionof an ad characterization vector and use of a unique consumer ID. Theconsumer ID is used to retrieve a consumer characterization vector, andthe correlation between the consumer characterization vector and the adcharacterization vector is used to determine the applicability of theadvertisement to the consumer. The price to be paid for presentation ofthe advertisement can be determined based on the degree of correlation.

[0015] The price to present an advertisement can increase withcorrelation, as may be typical when the content/opportunity provider isalso the profiling entity. The price can decrease with correlation whenthe consumer is the profiler, and is interested in, and willing tocharge less for seeing advertisements which are highly correlated withtheir demographics, lifestyle, and product preferences.

[0016] The present invention can be used to specify purchasers of aspecific product. In a preferred embodiment the advertisementcharacterization vector contains a description of a target marketincluding an indicator of a target product, i.e., purchasers of aparticular product type, brand, or product size. The advertisementcharacterization vector is correlated with a consumer characterizationvector which is retrieved based on a unique consumer ID. The correlationfactor is determined and indicates if the consumer is a purchaser of theproduct the advertisement is intended for. This feature can be used toidentify purchasers of a particular brand and can be used to target adsat those consumers to lure them away from their present productprovider. Similarly, this feature can be used to target ads to loyalconsumers to introduce them to a new product in a product family, ordifferent size of product.

[0017] One advantage of the present invention is that discretionarytarget market parameters can be specified and do not necessarily need tocorrespond to an existing market, but can reflect the various marketsegments for which the advertisement is targeted. The market segmentscan be designated by demographic characteristics or by productpreferences.

[0018] Another advantage of the present invention is that demographicsamples of present purchasers of a product are not required to definethe target market.

[0019] The present invention can be used to determine the applicabilityof an advertisement to a consumer based on demographics, productpreferences, or a combination of both.

[0020] In a preferred embodiment of the present invention thecorrelation is calculated as the scalar product of the adcharacterization vector and the consumer characterization vector. The adcharacterization vector and consumer characterization vector can becomposed of demographic characteristics, product purchasecharacteristics, or a combination of both.

[0021] In a preferred embodiment pricing for the displaying of saidadvertisement is developed based on the result of the correlationbetween the ad characterization vector and the consumer characterizationvector. In a first embodiment the pricing increases as a function of thecorrelation. This embodiment can represent the situation in which theparty which determines the correlation also controls the ability todisplay the advertisement.

[0022] In an alternate embodiment the price for displaying theadvertisement decreases as a function of the degree of correlation. Thisembodiment can represent the situation in which the consumer controlsaccess to the consumer characterization vector, and charges less to viewadvertisements which are highly correlated with their interests anddemographics. A feature of this embodiment is the ability of theconsumer to decrease the number of unwanted advertisements by charging ahigher price to view advertisements which are likely to be of lessinterest.

[0023] One advantage of the present invention is that it allowsadvertisements to be directed to new markets by setting specificparameters in the ad characterization vector, and does not requirespecific statistical knowledge regarding existing customers of similarproducts. Another advantage is that the system allows ads to be directedat consumers of a competing brand, or specific targeting at loyalcustomers. This feature can be useful for the introduction of a newproduct to an existing customer base.

[0024] Another advantage of the present invention is that thecorrelation can be performed by calculating a simple scalar (dot)product of the ad characterization and consumer characterizationvectors. A weighted sum or other statistical analysis is not required todetermine the applicability of the advertisement.

[0025] The present invention can be realized as a data processing systemand as a computer program. The invention can be realized on anindividual computer or can be realized using distributed computers withportions of the system operating on various computers.

[0026] An advantage of the present invention is the ability to directadvertisements to consumers which will find the advertisements ofinterest. This eliminates unwanted advertisements. Another advantage isthe ability of advertisers to target specific groups of potentialcustomers.

[0027] These and other features and objects of the invention will bemore fully understood from the following detailed description of thepreferred embodiments which should be read in light of the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which are incorporated in and form apart of the specification, illustrate the embodiments of the presentinvention and, together with the description serve to explain theprinciples of the invention.

[0029] In the drawings:

[0030]FIGS. 1A and 1B show user relationship diagrams for the presentinvention;

[0031]FIGS. 2A, 2B, 2C and 2D illustrate a probabilistic consumerdemographic characterization vector, a deterministic consumerdemographic characterization vector, a consumer product preferencecharacterization vector, and a storage structure for consumercharacterization vectors respectively;

[0032]FIGS. 3A and 3B illustrate an advertisement demographiccharacterization vector and an advertisement product preferencecharacterization vector respectively;

[0033]FIG. 4 illustrates a computer system on which the presentinvention can be realized;

[0034]FIG. 5 illustrates a context diagram for the present invention;

[0035]FIGS. 6A and 6B illustrate pseudocode updating the characteristicsvectors and for a correlation operation respectively;

[0036]FIG. 7 illustrates heuristic rules;

[0037]FIGS. 8A and 8B illustrate flowcharts for updating consumercharacterization vectors and a correlation operation respectively; and

[0038]FIG. 9 represents pricing as a function of correlation.

[0039]FIG. 10 illustrates a representation of a consumercharacterization as a set of basis vectors and an ad characterizationvector.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0040] In describing a preferred embodiment of the invention illustratedin the drawings, specific terminology will be used for the sake ofclarity. However, the invention is not intended to be limited to thespecific terms so selected, and it is to be understood that eachspecific term includes all technical equivalents which operate in asimilar manner to accomplish a similar purpose.

[0041] With reference to the drawings, in general, and FIGS. 1 through10 in particular, the method and apparatus of the present invention isdisclosed.

[0042]FIG. 1A shows a user relationship diagram which illustrates therelationships between a consumer profiling system and various entities.As can be seen in FIG. 1, a consumer 100 can receive information andadvertisements from a consumer personal computer (PC) 104, displayed ona television 108 which is connected to a set top 106, or can receive amailed ad 182.

[0043] Advertisements and information displayed on consumer PC 104 ortelevision 108 can be received over an Internet 150, or can be receivedover the combination of the Internet 150 with another telecommunicationsaccess system. The telecommunications access system can include but isnot limited to cable TV delivery systems, switched digital video accesssystems operating over telephone wires, microwave telecommunicationssystems, or any other medium which provides connectivity between theconsumer 100 and a content server 162 and ad server 146.

[0044] A content/opportunity provider 160 maintains the content server162 which can transmit content including broadcast programming across anetwork such as the Internet 150. Other methods of data transport can beused including private data networks and can connect the content sever160 through an access system to a device owned by consumer 100.

[0045] Content/opportunity provider 160 is termed such since if consumer100 is receiving a transmission from content server 162, thecontent/opportunity provider can insert an advertisement. For videoprogramming, content/opportunity provider is typically the cable networkoperator or the source of entertainment material, and the opportunity isthe ability to transmit an advertisement during a commercial break.

[0046] The majority of content that is being transmitted today is doneso in broadcast form, such as broadcast television programming(broadcast over the air and via cable TV networks), broadcast radio, andnewspapers. Although the interconnectivity provided by the Internet willallow consumer specific programming to be transmitted, there will stillbe a large amount of broadcast material which can be sponsored in partby advertising. The ability to insert an advertisement in a broadcaststream (video, audio, or mailed) is an opportunity for advertiser 144.Content can also be broadcast over the Internet and combined withexisting video services, in which case opportunities for the insertionof advertisements will be present.

[0047] Although FIG. 1A represents content/opportunity provider 160 andcontent server 162 as being independently connected to Internet 150,with the consumer's devices also being directly connected to theInternet 150, the content/opportunity provider 160 can also controlaccess to the subscriber. This can occur when the content/opportunityprovider is also the cable operator or telephone company. In suchinstances, the cable operator or telephone company can be providingcontent to consumer 100 over the cable operator/telephone company accessnetwork. As an example, if the cable operator has control over thecontent being transmitted to the consumer 100, and has programmed timesfor the insertion of advertisements, the cable operator is considered tobe a content/opportunity provider 160 since the cable operator canprovide advertisers the opportunity to access consumer 100 by insertingan advertisement at the commercial break.

[0048] In a preferred embodiment of the present invention, a pricingpolicy can be defined. The content/opportunity provider 160 can chargeadvertiser 144 for access to consumer 100 during an opportunity. In apreferred embodiment the price charged for access to consumer 100 bycontent/opportunity provider varies as a function of the applicabilityof the advertisement to consumer 100. In an alternate embodimentconsumer 100 retains control of access to the profile and charges forviewing an advertisement.

[0049] The content provider can also be a mailing company or printerwhich is preparing printed information for consumer 100. As an example,content server 162 can be connected to a printer 164 which creates amailed ad 182 for consumer 100. Alternatively, printer 164 can produceadvertisements for insertion into newspapers which are delivered toconsumer 100. Other printed material can be generated by printer 162 anddelivered to consumer 100 in a variety of ways.

[0050] Advertiser 144 maintains an ad server 146 which contains avariety of advertisements in the form of still video which can beprinted, video advertisements, audio advertisements, or combinationsthereof.

[0051] Profiler 140 maintains a consumer profile server 130 whichcontains the characterization of consumer 100. The consumer profilingsystem is operated by profiler 140, who can use consumer profile server130 or another computing device connected to consumer profile server 130to profile consumer 100.

[0052] Data to perform the consumer profiling is received from a pointof purchase 110. Point of purchase 110 can be a grocery store,department store, other retail outlet, or can be a web site or otherlocation where a purchase request is received and processed. In apreferred embodiment, data from the point of purchase is transferredover a public or private network 120, such as a local area networkwithin a store or a wide area network which connects a number ofdepartment or grocery stores. In an alternate embodiment the data frompoint of purchase 110 is transmitted over the Internet 150 to profiler140.

[0053] Profiler 140 may be a retailer who collects data from its stores,but can also be a third party who contracts with consumer 100 and theretailer to receive point of purchase data and to profile the consumer100. Consumer 100 may agree to such an arrangement based on theincreased convenience offered by targeted ads, or through a compensationarrangement in which they are paid on a periodic basis for revealingtheir specific purchase records.

[0054] Consumer profile server 130 can contain a consumer profile whichis determined from observation of the consumer's viewing habits ontelevision 108 or consumer PC 104. A method and apparatus fordetermining demographic and product preference information based on theconsumer's use of services such as cable television and Internet accessis described in the co-pending application entitled “Subscribercharacterization system,” filed on Dec. 3, 1998, with Ser. No.09/204,888 and in the co-pending application entitled “Client-serverbased subscriber characterization system,” filed on Dec. 3, 1998, withSer. No. 09/205,653, both of which are incorporated herein by referencebut which are not admitted to be prior art. When used herein, the termconsumer characterization vector also represents the subscribercharacterization vector described in the aforementioned applications.Both the consumer characterization vector and the subscribercharacterization vector contain demographic and product preferenceinformation which is related to consumer 100.

[0055]FIG. 1B illustrates an alternate embodiment of the presentinvention in which the consumer 100 is also profiler 140. Consumer 100maintains consumer profile server 130 which is connected to a network,either directly or through consumer PC 104 or set top 106. Consumerprofile server 130 can contain the consumer profiling system, or theprofiling can be performed in conjunction with consumer PC 104 or settop 106. A subscriber characterization system which monitors the viewinghabits of consumer 100 can be used in conjunction with the consumerprofiling system to create a more accurate consumer profile.

[0056] When the consumer 100 is also the profiler 140, as shown in FIG.1B, access to the consumer demographic and product preferencecharacterization is controlled exclusively by consumer 100, who willgrant access to the profile in return for receiving an increasedaccuracy of ads, for cash compensation, or in return for discounts orcoupons on goods and services.

[0057]FIG. 2A illustrates an example of a probabilistic demographiccharacterization vector. The demographic characterization vector is arepresentation of the probability that a consumer falls within a certaindemographic category such as an age group, gender, household size, orincome range.

[0058] In a preferred embodiment the demographic characterization vectorincludes interest categories. The interest categories may be organizedaccording to broad areas such as music, travel, and restaurants.Examples of music interest categories include country music, rock,classical, and folk. Examples of travel categories include “travels toanother state more than twice a year,” and travels by plane more thantwice a year.”

[0059]FIG. 2B illustrates a deterministic demographic characterizationvector. The deterministic demographic characterization vector is arepresentation of the consumer profile as determined from deterministicrather than probabilistic data. As an example, if consumer 100 agrees toanswer specific questions regarding age, gender, household size, income,and interests the data contained in the consumer characterization vectorwill be deterministic.

[0060] As with probabilistic demographic characterization vectors, thedeterministic demographic characterization vector can include interestcategories. In a preferred embodiment, consumer 100 answers specificquestions in a survey generated by profiler 140 and administered overthe phone, in written form, or via the Internet 150 and consumer PC 104.The survey questions correspond either directly to the elements in theprobabilistic demographic characterization vector, or can be processedto obtain the deterministic results for storage in the demographiccharacterization vector.

[0061]FIG. 2C illustrates a product preference vector. The productpreference represents the average of the consumer preferences over pastpurchases. As an example, a consumer who buys the breakfast cerealmanufactured by Post under the trademark ALPHABITS about twice as oftenas purchasing the breakfast cereal manufactured by Kellogg under thetrademark CORN FLAKES, but who never purchases breakfast cerealmanufactured by General Mills under the trademark WHEATIES, would have aproduct preference characterization such as that illustrated in FIG. 2C.As shown in FIG. 2C, the preferred size of the consumer purchase of aparticular product type can also be represented in the productpreference vector.

[0062]FIG. 2D represents a data structure for storing the consumerprofile, which can be comprised of a consumer ID field 237, adeterministic demographic data field 239, a probabilistic demographicdata field 241, and one or more product preference data fields 243. Asshown in FIG. 2D, the product preference data field 243 can be comprisedof multiple fields arranged by product categories 253.

[0063] Depending on the data structure used to store the informationcontained in the vector, any of the previously mentioned vectors may bein the form of a table, record, linked tables in a relational database,series of records, or a software object.

[0064] The consumer ID 512 can be any identification value uniquelyassociated with consumer 100. In a preferred embodiment consumer ID 512is a telephone number, while in an alternate embodiment consumer ID 512is a credit card number. Other unique identifiers include consumer namewith middle initial or a unique alphanumeric sequence, the consumeraddress, social security number.

[0065] The vectors described and represented in FIGS. 2A-C form consumercharacterization vectors that can be of varying length and dimension,and portions of the characterization vector can be used individually.Vectors can also be concatenated or summed to produce longer vectorswhich provide a more detailed profile of consumer 100. A matrixrepresentation of the vectors can be used, in which specific elements,such a product categories 253, are indexed. Hierarchical structures canbe employed to organize the vectors and to allow hierarchical searchalgorithms to be used to locate specific portions of vectors.

[0066]FIGS. 3A and 3B represent an ad demographics vector and an adproduct preference vector respectively. The ad demographics vector,similar in structure to the demographic characterization vector, is usedto target the ad by setting the demographic parameters in the addemographics vector to correspond to the targeted demographic group. Asan example, if an advertisement is developed for a market which is the18-24 and 24-32 age brackets, no gender bias, with a typical householdsize of 2-5, and income typically in the range of $20,000-$50,000, thead demographics vector would resemble the one shown in FIG. 3A. The addemographics vector represents a statistical estimate of who the ad isintended for, based on the advertisers belief that the ad will bebeneficial to the manufacturer when viewed by individuals in thosegroups. The benefit will typically be in the form of increased sales ofa product or increased brand recognition. As an example, an “image ad”which simply shows an artistic composition but which does not directlysell a product may be very effective for young people, but may beannoying to older individuals. The ad demographics vector can be used toestablish the criteria which will direct the ad to the demographic groupof 18-24 year olds.

[0067]FIG. 3B illustrates an ad product preference vector. The adproduct preference vector is used to select consumers which have aparticular product preference. In the example illustrated in FIG. 3B,the ad product preference vector is set so that the ad can be directedat purchasers of ALPHABITS and WHEATIES, but not at purchasers of CORNFLAKES. This particular setting would be useful when the advertiserrepresents Kellogg and is charged with increasing sales of CORN FLAKES.By targeting present purchasers of ALPHABITS and WHEATIES, theadvertiser can attempt to sway those purchasers over to the Kelloggbrand and in particular convince them to purchase CORN FLAKES. Giventhat there will be a payment required to present the advertisement, inthe form of a payment to the content/opportunity provider 160 or to theconsumer 100, the advertiser 144 desires to target the ad and therebyincrease its cost effectiveness.

[0068] In the event that advertiser 144 wants to reach only thepurchasers of Kellogg's CORN FLAKES, that category would be set at ahigh value, and in the example shown would be set to 1. As shown in FIG.3B, product size can also be specified. If there is no preference tosize category the values can all be set to be equal. In a preferredembodiment the values of each characteristic including brand and sizeare individually normalized.

[0069] Because advertisements can be targeted based on a set ofdemographic and product preference considerations which may not berepresentative of any particular group of present consumers of theproduct, the ad characterization vector can be set to identify a numberof demographic groups which would normally be considered to beuncorrelated. Because the ad characterization vector can have targetprofiles which are not representative of actual consumers of theproduct, the ad characterization vector can be considered to havediscretionary elements. When used herein the term discretionary refersto a selection of target market characteristics which need not berepresentative of an actual existing market or single purchasingsegment.

[0070] In a preferred embodiment the consumer characterization vectorsshown in FIGS. 2A-C and the ad characterization vectors represented inFIGS. 3A and 3B have a standardized format, in which each demographiccharacteristic and product preference is identified by an indexedposition. In a preferred embodiment the vectors are singly indexed andthus represent coordinates in n-dimensional space, with each dimensionrepresenting a demographic or product preference characteristic. In thisembodiment a single value represents one probabilistic or deterministicvalue (e.g. the probability that the consumer is in the 18-24 year oldage group, or the weighting of an advertisement to the age group).

[0071] In an alternate embodiment a group of demographic or productcharacteristics forms an individual vector. As an example, agecategories can be considered a vector, with each component of the vectorrepresenting the probability that the consumer is in that age group. Inthis embodiment each vector can be considered to be a basis vector forthe description of the consumer or the target ad. The consumer or adcharacterization is comprised of a finite set of vectors in a vectorspace that describes the consumer or advertisement.

[0072]FIG. 4 shows the block diagram of a computer system for arealization of the consumer profiling system. A system bus 422transports data amongst the CPU 203, the RAM 204, Read Only Memory—BasicInput Output System (ROM-BIOS) 406 and other components. The CPU 203accesses a hard drive 400 through a disk controller 402. The standardinput/output devices are connected to the system bus 422 through the I/Ocontroller 201. A keyboard is attached to the I/O controller 201 througha keyboard port 416 and the monitor is connected through a monitor port418. The serial port device uses a serial port 420 to communicate withthe I/O controller 201. Industry Standard Architecture (ISA) expansionslots 408 and Peripheral Component Interconnect (PCI) expansion slots410 allow additional cards to be placed into the computer. In apreferred embodiment, a network card is available to interface a localarea, wide area, or other network. The computer system shown in FIG. 4can be part of consumer profile server 130, or can be a processorpresent in another element of the network.

[0073]FIG. 5 shows a context diagram for the present invention. Contextdiagrams are useful in illustrating the relationship between a systemand external entities. Context diagrams can be especially useful indeveloping object oriented implementations of a system, although use ofa context diagram does not limit implementation of the present inventionto any particular programming language. The present invention can berealized in a variety of programming languages including but not limitedto C, C++, Smalltalk, Java, Perl, and can be developed as part of arelational database. Other languages and data structures can be utilizedto realize the present invention and are known to those skilled in theart.

[0074] Referring to FIG. 5, in a preferred embodiment consumer profilingsystem 500 is resident on consumer profile server 130. Point of purchaserecords 510 are transmitted from point of purchase 110 and stored onconsumer profile server 130. Heuristic rules 530, pricing policy 570,and consumer profile 560 are similarly stored on consumer profile server130. In a preferred embodiment advertisement records 540 are stored onad server 146 and connectivity between advertisement records 540 andconsumer profiling system 500 is via the Internet or other network.

[0075] In an alternate embodiment the entities represented in FIG. 5 arelocated on servers which are interconnected via the Internet or othernetwork.

[0076] Consumer profiling system 500 receives purchase information froma point of purchase, as represented by point of purchase records 510.The information contained within the point of purchase records 510includes a consumer ID 512, a product ID 514 of the purchased product,the quantity 516 purchased and the price 518 of the product. In apreferred embodiment, the date and time of purchase 520 are transmittedby point of purchase records 510 to consumer profiling system 500.

[0077] The consumer profiling system 500 can access the consumer profile560 to update the profiles contained in it. Consumer profiling system500 retrieves a consumer characterization vector 562 and a productpreference vector 564. Subsequent to retrieval one or more dataprocessing algorithms are applied to update the vectors. An algorithmfor updating is illustrated in the flowchart in FIG. 8A. The updatedvectors termed herein as new demographic characterization vector 566 andnew product preference 568 are returned to consumer profile 560 forstorage.

[0078] Consumer profiling system 500 can determine probabilisticconsumer demographic characteristics based on product purchases byapplying heuristic rules 519. Consumer profiling system 500 provides aproduct ID 514 to heuristic rules records 530 and receives heuristicrules associated with that product. Examples of heuristic rules areillustrated in FIG. 7.

[0079] In a preferred embodiment of the present invention, consumerprofiling system 500 can determine the applicability of an advertisementto the consumer 100. For determination of the applicability of anadvertisement, a correlation request 546 is received by consumerprofiling system 500 from advertisements records 540, along withconsumer ID 512. Advertisements records 540 also provide advertisementcharacteristics including an ad demographic vector 548, an ad productcategory 552 and an ad product preference vector 554.

[0080] Application of a correlation process, as will be described inaccordance with FIG. 8B, results in a demographic correlation 556 and aproduct correlation 558 which can be returned to advertisement records540. In a preferred embodiment, advertiser 144 uses product correlation558 and demographic correlation 556 to determine the applicability ofthe advertisement and to determine if it is worth purchasing theopportunity. In a preferred embodiment, pricing policy 570 is utilizedto determine an ad price 570 which can be transmitted from consumerprofiling system 500 to advertisement records 540 for use by advertiser144.

[0081] Pricing policy 570 is accessed by consumer profiling system 500to obtain ad price 572. Pricing policy 570 takes into considerationresults of the correlation provided by the consumer profiling system500. An example of pricing schemes are illustrated in FIG. 9

[0082]FIGS. 6A and 6B illustrate pseudocode for the updating process andfor a correlation operation respectively. The updating process involvesutilizing purchase information in conjunction with heuristic rules toobtain a more accurate representation of consumer 100, stored in theform of a new demographic characterization vector 562 and a new productpreference vector 568.

[0083] As illustrated in the pseudocode in FIG. 6A the point of purchasedata are read and the products purchase are integrated into the updatingprocess. Consumer profiling system 500 retrieves a product demographicsvector obtained from the set of heuristic rules 519 and applies theproduct demographics vector to the demographics characterization vector562 and the product preference vector 564 from the consumer profile 560.

[0084] The updating process as illustrated by the pseudocode in FIG. 6Autilizes a weighting factor which determines the importance of thatproduct purchase with respect to all of the products purchased in aparticular product category. In a preferred embodiment the weight iscomputed as the ratio of the total of products with a particular productID 514 purchased at that time, to the product total purchase, which isthe total quantity of the product identified by its product ID 514purchased by consumer 100 identified by its consumer ID 512, purchasedover an extended period of time. In a preferred embodiment the extendedperiod of time is one year.

[0085] In the preferred embodiment the product category total purchaseis determined from a record containing the number of times that consumer100 has purchased a product identified by a particular product ID.

[0086] In an alternate embodiment other types of weighting factors,running averages and statistical filtering techniques can be used to usethe purchase data to update the demographic characterization vector. Thesystem can also be reset to clear previous demographic characterizationvectors and product preference vectors.

[0087] The new demographic characterization vector 566 is obtained asthe weighted sum of the product demographics vector and the demographiccharacterization vector 562. The same procedure is performed to obtainthe new product preference vector 568. Before storing those new vectors,a normalization is performed on the new vectors. When used herein theterm product characterization information refers to product demographicsvectors, product purchase vectors or heuristic rules, all of which canbe used in the updating process. The product purchase vector refers tothe vector which represents the purchase of an item represented by aproduct ID. As an example, a product purchase vector for the purchase ofKellogg's CORN FLAKES in a 32 oz. size has a product purchase vectorwith a unity value for Kellogg's CORN FLAKES and in the 32 oz. size. Inthe updating process the weighted sum of the purchase as represented bythe product purchase vector is added to the product preference vector toupdate the product preference vector, increasing the estimatedprobability that the consumer will purchase Kellogg's CORN FLAKES in the32 oz. size.

[0088] In FIG. 6B the pseudocode for a correlation process isillustrated. Consumer profiling system 500, after receiving the productcharacteristics and the consumer ID 512 from the advertisement recordsretrieves the consumer demographic characterization vector 562 and itsproduct preference vector 564. The demographic correlation is thecorrelation between the demographic characterization vector 562 and thead demographics vector. The product correlation is the correlationbetween the ad product preference vector 554 and the product preferencevector 564.

[0089] In a preferred embodiment the correlation process involvescomputing the dot product between vectors. The resulting scalar is thecorrelation between the two vectors.

[0090] In an alternate embodiment, as illustrated in FIG. 10, the basisvectors which describe aspects of the consumer can be used to calculatethe projections of the ad vector on those basis vectors. In thisembodiment, the result of the ad correlation can itself be in vectorform whose components represent the degree of correlation of theadvertisement with each consumer demographic or product preferencefeature. As shown in FIG. 10 the basis vectors are the age of theconsumer 1021, the income of the consumer 1001, and the family size ofthe consumer 1031. The ad characterization vector 1500 represents thedesired characteristics of the target audience, and can include productpreference as well as demographic characteristics.

[0091] In this embodiment the degree of orthogonality of the basisvectors will determine the uniqueness of the answer. The projections onthe basis vectors form a set of data which represent the correspondingvalues for the parameter measured in the basis vector. As an example, ifhousehold income is one basis vector, the projection of the adcharacterization vector on the household income basis vector will returna result indicative of the target household income for thatadvertisement.

[0092] Because basis vectors cannot be readily created from some productpreference categories (e.g. cereal preferences) an alternaterepresentation to that illustrated in FIG. 2C can be utilized in whichthe product preference vector represents the statistical average ofpurchases of cereal in increasing size containers. This vector can beinterpreted as an average measure of the cereal purchased by theconsumer in a given time period.

[0093] The individual measurements of correlation as represented by thecorrelation vector can be utilized in determining the applicability ofthe advertisement to the subscriber, or a sum of correlations can begenerated to represent the overall applicability of the advertisement.

[0094] In a preferred embodiment individual measurements of thecorrelations, or projections of the ad characteristics vector on theconsumer basis vectors, are not made available to protect consumerprivacy, and only the absolute sum is reported. In geometric terms thiscan be interpreted as disclosure of the sum of the lengths of theprojections rather than the actual projections themselves.

[0095] In an alternate embodiment the demographic and product preferenceparameters are grouped to form sets of paired scores in which elementsin the consumer characterization vector are paired with correspondingelements of the ad characteristics vector. A correlation coefficientsuch as the Pearson product-moment correlation can be calculated. Othermethods for correlation can be employed and are well known to thoseskilled in the art.

[0096] When the consumer characterization vector and the adcharacterization vector are not in a standardized format, atransformation can be performed to standardize the order of thedemographic and product preferences, or the data can be decomposed intosets of basis vectors which indicate particular attributes such as age,income or family size.

[0097]FIG. 7 illustrates an example of heuristic rules including rulesfor defining a product demographics vector. From the productcharacteristics, a probabilistic determination of household demographicscan be generated. Similarly, the monthly quantity purchased can be usedto estimate household size. The heuristic rules illustrated in FIG. 7serve as an example of the types of heuristic rules which can beemployed to better characterize consumer 100 as a result of theirpurchases. The heuristic rules can include any set of logic tests,statistical estimates, or market studies which provide the basis forbetter estimating the demographics of consumer 100 based on theirpurchases.

[0098] In FIG. 8A the flowchart for updating the consumercharacterization vectors is depicted. The system receives data from thepoint of purchase at receive point of purchase information step 800. Thesystem performs a test to determine if a deterministic demographiccharacterization vector is available at deterministic demographicinformation available step 810 and, if not, proceeds to update thedemographic characteristics.

[0099] Referring to FIG. 8A, at read purchase ID info step 820, theproduct ID 514 is read, and at update consumer demographiccharacterization vector step 830, an algorithm such as that representedin FIG. 6A is applied to obtain a new demographic characterizationvector 566, which is stored in the consumer profile 560 at store updateddemographic characterization vector step 840.

[0100] The end test step 850 can loop back to the read purchase ID info820 if all the purchased products are not yet processed for updating, orcontinue to the branch for updating the product preference vector 564.In this branch, the purchased product is identified at read purchase IDinfo step 820. An algorithm, such as that illustrated in FIG. 6A forupdating the product preference vector 564, is applied in update productpreference vector step 870. The updated vector is stored in consumerprofile 560 at store product preference vector step 880. This process iscarried out until all the purchased items are integrated in the updatingprocess.

[0101]FIG. 8B shows a flowchart for the correlation process. At step 900the advertisement characteristics described earlier in accordance withFIG. 5 along with the consumer ID are received by consumer profilingsystem 500. At step 910 the demographic correlation 556 is computed andat step 920 the product preference correlation 558 is computed. Anillustrative example of an algorithm for correlation is presented inFIG. 6b. The system returns demographic correlation 556 and productpreference correlation 558 to the advertisement records 540 beforeexiting the procedure at end step 950.

[0102]FIG. 9 illustrates two pricing schemes, one forcontent/opportunity provider 160 based pricing 970, which showsincreasing cost as a function of correlation. In this pricing scheme,the higher the correlation, the more the content/opportunity provider160 charges to air the advertisement.

[0103]FIG. 9 also illustrates consumer based pricing 960, which allows aconsumer to charge less to receive advertisements which are more highlycorrelated with their demographics and interests.

[0104] As an example of the industrial applicability of the invention, aconsumer 100 can purchase items in a grocery store which also acts as aprofiler 140 using a consumer profiling system 500. The purchase recordis used by the profiler to update the probabilistic representation ofcustomer 100, both in terms of their demographics as well as theirproduct preferences. For each item purchased by consumer 100, productcharacterization information in the form of a product demographicsvector and a product purchase vector is used to update the demographiccharacterization vector and the product preference vector for consumer100.

[0105] A content/opportunity provider 160 may subsequently determinethat there is an opportunity to present an advertisement to consumer100. Content/opportunity provider 160 can announce this opportunity toadvertiser 144 by transmitting the details regarding the opportunity andthe consumer ID 512. Advertiser 144 can then query profiler 140 bytransmitting consumer ID 512 along with advertisement specificinformation including the correlation request 546 and ad demographicsvector 548. The consumer profiling system 500 performs a correlation anddetermines the extent to which the ad target market is correlated withthe estimated demographics and product preferences of consumer 100.Based on this determination advertiser 144 can decide whether topurchase the opportunity or not.

[0106] Although this invention has been illustrated by reference tospecific embodiments, it will be apparent to those skilled in the artthat various changes and modifications may be made which clearly fallwithin the scope of the invention. The invention is intended to beprotected broadly within the spirit and scope of the appended claims.

What is claimed is:
 1. A computer implemented method for selecting atargeted advertisement to be presented to a consumer by determiningsimilarities between a profile of the consumer which is generated inpart from purchase records of the consumer and a profile for each of aplurality of advertisements, wherein the profile for each advertisementidentifies discretionary characteristics of an intended target market ofthe advertisement, the method comprising: receiving the consumerprofile; receiving the plurality of advertisement profiles; comparingeach advertisement profile with the consumer profile to determine acorrelation value between each advertisement profile and the consumerprofile; and selecting a targeted advertisement to be presented to aparticular consumer based on correlation values.
 2. The method of claim1 , wherein each advertisement profile is identified by a uniqueidentifier.
 3. The method of claim 1 , wherein each advertisementprofile includes a demographic characterization of an intended targetmarket; the consumer profile includes a demographic characterization ofthe consumer; and said comparing includes determining a correlationvalue between the demographic characterization of the intended targetmarket and the demographic characterization of the consumer.
 4. Themethod of claim 1 , wherein each advertisement profile includes aproduct preference characterization of the intended target market; theconsumer profile includes a product preference characterization of theconsumer; and said comparing includes determining a correlation valuebetween the product preference characterization of the intended targetmarket and the product preference characterization of the consumer. 5.The method of claim 1 , further comprising determining a price topresent the targeted advertisement to the consumer, wherein the price isbased on the determined correlation value.
 6. The method of claim 5 ,wherein the price is an increasing monotonic function of the correlationvalue.
 7. The method of claim 6 , wherein the price is a decreasingmonotonic function of the correlation value.
 8. The method of claim 1 ,wherein the purchase records are accumulated in at least one externaldatabase.
 9. The method of claim 1 , wherein the purchase records areaccumulated from multiple point-of-sale transactions.
 10. The method ofclaim 9 , wherein the multiple point-of-sale transactions are transactedat a plurality of locations.
 11. The method of claim 1 , furthercomprising presenting the targeted advertisement to the consumer via atelevision.
 12. The method of claim 1 , wherein the consumer profileincludes one or more vectors, each vector representing probabilisticinformation associated with the consumer.
 13. The method of claim 12 ,wherein the probabilistic information includes one or more probabilisticdemographic characterization vectors, each probabilistic demographiccharacterization vector based on a representation of a probability thata consumer falls within a certain demographic category.
 14. The methodof claim 13 , wherein each demographic characterization vector isfurther based on one or more interest categories.
 15. The method ofclaim 12 , wherein the probabilistic information includes one or moreproduct preference categories, wherein each product preference categoryrepresents the average of consumer preferences over past purchases. 16.The method of claim 1 , wherein the consumer profile further includesdeterministic information corresponding to the consumer.
 17. The methodof claim 1 , wherein the consumer profile is further based on one ormore television viewing transactions.
 18. The method of claim 1 ,wherein said retrieving the consumer profile comprises: retrieving adetailed transaction record, wherein the detailed transaction recordincludes an inventory of multiple transactions which occurred over apredetermined time interval; and generating the consumer profile fromthe detailed transaction record.
 19. The method of claim 18 , wherein:said retrieving the consumer profile further comprises retrieving a setof heuristic rules associated with transactions within the detailedtransaction record, the set of heuristic rules defining a probabilisticmeasure of demographic characteristics of a person performing thetransactions; and said generating comprises generating the consumerprofile from the detailed purchase transaction record and the set ofheuristic rules.
 20. The method of claim 18 , wherein said retrieving adetailed transaction record includes: storing the multiple transactionsof the consumer; and generating the detailed transaction record based onthe stored transactions.
 21. A computer system for selecting a targetedadvertisement to be presented to a consumer by determining similaritiesbetween a profile of the consumer and a plurality of advertisementprofiles, each advertisement profile identifying discretionarycharacteristics of an intended target market of the advertisement, thesystem comprising: a storage medium; means for receiving a plurality ofadvertisement profiles; means for retrieving a consumer profile, whereinthe consumer profile is derived in part from corresponding purchaserecords; means for calculating a correlation value between eachadvertisement profile and the consumer profile; and means for selectinga targeted advertisement to present to the consumer based on thecorrelation values.
 22. The system of claim 21 , further comprisingmeans for accumulating detailed purchase records from a plurality ofpoint of sale transactions, wherein the consumer profile is derived inpart from the detailed purchase records.
 23. The system of claim 21 ,further comprising: means for retrieving a pricing function; and meansfor determining a price for displaying the targeted advertisement to theconsumer, wherein the price is determined from the correlation value andthe pricing function.
 24. The system of claim 21 , further comprisingmeans for transmitting the targeted advertisement to the consumer. 25.The system of claim 21 , wherein the means for retrieving a consumerprofile includes: means for retrieving a detailed purchase record of theconsumer; means for retrieving a set of heuristic rules associated withproducts included in the detailed purchase records, wherein the set ofheuristic rules define a probabilistic measure of demographiccharacteristics of a purchaser of corresponding products; and means forgenerating the consumer profile from the detailed purchase records andthe set of heuristic rules.
 26. A computer program embodied on acomputer-readable medium for selecting a targeted advertisement for aconsumer by comparing a profile of the consumer to a profile of each ofa plurality of advertisements, wherein each advertisement profileidentifies specific characteristics of an intended target market of theadvertisement, the computer program comprising: a source code segmentfor receiving a plurality of advertisement profiles; a source codesegment for retrieving the consumer profile, wherein the consumerprofile is partly derived from purchase records; and a source codesegment for determining a correlation value between each advertisementprofile and the consumer profile; and a source code segment forselecting a targeted advertisement to present to the consumer based oncorrelation values.
 27. The computer program of claim 26 , furthercomprising a source code segment for transmitting the determinedcorrelation values to advertisers.
 28. A computer implemented method forpresenting selected advertisements to a consumer based on how applicablethe selected advertisements are to the consumer, the method comprising:receiving a consumer profile, wherein the consumer profile identifiesdiscretionary characteristics of the consumer and is generated based ontransactions made by the consumer; receiving a plurality ofadvertisement profiles, wherein each advertisement profile identifiesdiscretionary characteristics of consumers in an intended target marketfor an associated advertisement; determining a correlation value betweeneach advertisement profile and the consumer profile; and selecting oneor more advertisements to be presented to the consumer based on thecorrelation values; and presenting the one or more selectedadvertisements to the consumer.
 29. The method of claim 28 , wherein thediscretionary characteristics identified in the consumer profile includea demographic characterization of the consumer; the discretionarycharacteristics identified in each advertisement profile include ademographic characterization of consumers in the intended target market;and said determining includes determining a correlation value betweeneach demographic characterization of consumers in the intended targetmarket and the demographic characterization of the consumer.
 30. Themethod of claim 28 , wherein said receiving a consumer profile includes:receiving a transaction record for each of the transactions made by theconsumer; retrieving a set of heuristic rules associated with eachtransaction, the set of heuristic rules defining a probabilistic measureof demographic characteristics of a person performing the transaction;and generating the consumer profile from the transaction records and theset of heuristic rules.
 31. The method of claim 28 , wherein saidpresenting the one or more selected advertisements to the consumerincludes presenting the one or more selected advertisements to theconsumer via a television.
 32. The method of claim 28 , wherein thetransactions made by the consumer include at least one of the followingtransactions: point of sale purchases; Internet browsing sessions;Internet purchases; and television viewing sessions.
 33. The method ofclaim 28 , wherein said selecting includes selecting the one or moreadvertisements with highest correlation values.
 34. The method of claim28 , wherein said selecting includes selecting the one or moreadvertisements exceeding a predetermined correlation value.